Virtual array systems use a moving antenna to synthesize a large virtual aperture and, thus, achieve high resolution images. A single pass virtual array system is capable of imaging a two-dimensional (2D) range-azimuth reflectivity of a scene without any elevation resolution. However, the three-dimensional (3D) structure of the scene, such as features in a 3D terrain, is not preserved. The 2D image is essentially a projection of the 3D reflectivity space into the 2D range-azimuth imaging plane. This projection may cause several artifacts. For example with layover artifacts, several terrain patches with different elevation angles are mapped to the same range-azimuth resolution cell. With shadowing artifacts, certain parts of the scene are not visible to the virtual array system because another structures are in the path of illumination. These artifacts cannot be resolved by a single pass, even using interferometric array imaging techniques.
In order to perform 3D imaging, multi-baseline data are necessary in the elevation dimension. The multi-baseline observations can be acquired by multiple passes of a single-channel virtual array platform. This idea has been realized with the launch of the TerraSAR-X and the COSMO-Skymed satellites. With the additional elevation dimension, a 3D image is able to separate multiple scatterers in the scene along elevation, even when the scatterers are present in the same range-azimuth location. However, 3D imagery requires several trade-offs. First, to acquire with multiple baselines, the single-channel platform needs to perform several passes over the scene. This makes data collection time consuming and expensive. Second, the elevation resolution is much worse than that of range and azimuth due to the small elevation aperture, also known as a “tight orbital tube” in virtual array sensors.
As shown in FIG. 1, a conventional 3D SAR system for generating a 3D image using multiple baseline arrays of antennas 101 mounted on a single radar platform moving 103 in a 3D elevation, range and azimuth space. The angular aperture of the baselines in the azimuth-elevation plane can be denoted by θ. The figure shows point scatterers (reflectors) 102 for different elevations in the scene.
FIG. 2 show a conventional 3D imaging process for the system of FIG. 1. Data 201 are acquired at each baseline (1, . . . , N) 101. 2D SAR imaging 210 is applied independently to each data 201 to construct 2D images (I1, I2, . . . , IN) 215. The images are registered and aligned 220, followed by 3D image reconstruction 230 to obtain a 3D image 240 of the scene.
With the additional elevation dimension, the 3D image can separate multiple scatterers along the elevation dimension, even when the scatterers are present in the same range-azimuth location. However, 3D imagery requires several trade-offs.
First, to acquire images at multiple baselines, the platform needs to perform several passes over the area of interest. This makes data collection time consuming and very expensive. Second, the elevation resolution is much worse than that of range and azimuth due to the small elevation aperture, which is known as a tight orbital tube, of modern SAR sensors, e.g., ≈500 meters diameter.
The elevation resolution can be improved using compressive sensing (CS) based approaches, see Zhu et al., “Tomographic SAR inversion by L1-norm regularization—the compressive sensing approach,” IEEE Trans. Geoscience and Remote Sensing, vol. 48(10), pp. 3839-3846, October 2010. That CS approach uses multiple baselines, a single PRF of a single SAR platform. In that method, a 2D range-azimuth image is reconstructed for each baseline. Then, compressive sensing based method is used improve elevation resolution. That method only considers sparsity for each 2D range-azimuth pixel.
In U.S. application Ser. No. 14/202,449, “System and Method for 3D SAR Imaging using Compressive Sensing with Multi-Platform, Multi-Baseline and Multi-PRF Data,” filed by Liu et al. on Mar. 10, 2014, now U.S. Pat. No. 9,864,054, a compressive sensing based method is considered to reconstruct 3D images. However, the baselines are restricted to the azimuth-elevation plane.